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scientific-visualization

Generate publication-ready multi-panel scientific figures with journal-compliant styling and export.

7|1|Updated Jan 25, 2026
Search Tags:#visualization#plotly#matplotlib#seaborn#publication#color-palettes
One-click install
npx skills add https://github.com/daidong/PiPilot --skill scientific-visualization-daidong
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/daidong/PiPilot/tree/main/lib/skills/builtin/scientific-visualization
Command: npx skills add https://github.com/daidong/PiPilot --skill scientific-visualization-daidong

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, numpy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Meta-skill coordinates styling, color palettes, and export settings to produce publication-quality figures across Matplotlib, Seaborn, and Plotly, ensuring consistency and efficiency in scientific visuals.

Core Features & Use Cases

  • Publication-ready multi-panel figures with journal-style typography and layout
  • Colorblind-friendly palettes and accessible color choices compatible with grayscale viewing
  • Automated export and configuration for journal requirements, size checks, and style presets
  • Integration with notebooks and pipelines to produce figures for manuscripts, slides, and supplementary materials

Quick Start

Create a simple multi-panel figure by applying a publication style and exporting to PDF.

Frequently Asked Questions about scientific-visualization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create publication-ready figures with Matplotlib that meet journal formatting requirements?▼

Publication-ready figures are generated by coordinating styling, color palettes, and export settings to produce journal-compliant outputs. This Skill applies journal sizing, font embedding, and automated export utilities across Matplotlib, Seaborn, and Plotly to ensure clean, consistent scientific visuals.

Can I export Matplotlib and Plotly figures to vector formats like PDF and EPS for manuscript submission?▼

Yes, you can export figures to vector and raster formats including PDF, EPS, SVG, TIFF, and PNG. The export utilities handle font embedding and journal sizing requirements, ensuring outputs meet specific journal submission standards for scientific manuscripts.

What is the best way to build colorblind-friendly multi-panel figures in Python for scientific publications?▼

Building colorblind-friendly multi-panel figures involves applying accessible color palettes compatible with grayscale viewing. This Skill provides journal-style typography and layout coordination across Matplotlib, Seaborn, and Plotly to ensure visual consistency and accessibility in multi-panel scientific figures.

Does this publication styling workflow work within Jupyter notebook environments?▼

Yes, the workflow integrates directly with notebook environments and pipelines. It applies publication style presets, color palettes, and export configurations to figures generated in notebooks, supporting outputs for manuscripts, slides, and supplementary materials.

Do I need Seaborn and Plotly installed to use this Skill, or are Matplotlib and NumPy enough?▼

Matplotlib and NumPy are the required dependencies, while Seaborn and Plotly are supported for extended styling and visualization options. You can generate publication-ready figures using Matplotlib alone, but the Skill coordinates styling across all three libraries if they are available.

How do I configure journal-compliant sizing and automated export settings for scientific figures?▼

Journal-compliant sizing and automated export are configured through provided utilities that manage style presets, size checks, and export settings. The Skill automates the configuration process to ensure figures meet specific journal requirements for dimensions, fonts, and file formats.

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